01099540

Component

https://doi.org/10.3141/2085-08

http://scholar.google.com...alma&publication_year=2008

Nonrecurrent congestion in transportation networks occurs as a consequence of stochastic factors affecting demand and supply. Intelligent transportation systems such as advanced traveler information systems and advanced traffic management systems are designed to reduce the impacts of nonrecurrent congestion by providing information to a fraction of users or by controlling the variability of traffic flows. For these reasons, the design of these systems requires a reliable forecast of nonrecurrent congestion. A new method is proposed to measure the impacts of nonrecurrent congestion on travel costs by taking risk aversion into account. The traffic model is based on the dynamic traffic simulation model METROPOLIS. Incidents are generated randomly by reducing the capacity of the network. Users can instantaneously adapt to the unexpected travel conditions or can also change their behavior through a day-to-day adjustment process. Comparisons with incident-free simulations provide a benchmark for potential travel time savings that can be brought about by a state-of-the-art information system. The impact of variable travel conditions is measured by describing the willingness to pay to avoid risky or unreliable journeys. Indeed, for risk-averse drivers, any uncertainty corresponds to a utility loss. This utility loss is computed for several levels of network disruption. The main result of the study is that the utility loss due to uncertainty is of the same order of magnitude as the total travel costs.

01120896

English

pp 67-75

2008

Transportation Research Record: Journal of the Transportation Research Board

9780309125963

Print

Figures (5) ; References (25) ; Tables (2)

Advanced traffic management systems; Advanced traveler information systems; Nonrecurrent congestion; Traffic congestion; Traffic models; Traffic simulation; Travel costs; Traveler information and communication systems; Uncertainty

Willingness to pay

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 29 2008 3:26PM

• Adaptive Route Choice Models in Stochastic Time-Dependent Networks
• Dynamic Network Loading Model with Explicit Traffic Wave Tracking
• Dynamic Origin–Destination Demand Estimation Using Turning Movement Counts
• Effectiveness of Origin–Destination Matrix Correction Procedure Using Traffic Counts
• Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment
• Generalized Wardrop Principle and Its Application in Regional Transportation
• Incorporating Within-Day Transitions in Simultaneous Offline Estimation of Dynamic Origin–Destination Flows Without Assignment Matrices
• Lower-Bound Solution Algorithm for Equilibrium Signal-Setting Problem
• Modeling User Responses to Pricing: Simultaneous Route and Departure Time Network Equilibrium with Heterogeneous Users
• Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model
• Robust Pricing of Transportation Networks Under Uncertain Demand
• Simultaneous Perturbation Stochastic Approximation Algorithm for Solving Stochastic Problems of Transportation Network Analysis: Performance Evaluation
• Stochastic Multiclass Traffic Assignment with Consideration of Risk-Taking Behaviors
• Stochastic User Equilibrium Model with Implicit Travel Time Budget Constraint